Seurat Example
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library(dplyr)
library(Seurat)
library(patchwork)
pbmc.data = Read10X(data.dir = "~/Desktop/USC/Fall_2020/TRGN_510/Seurat_guided_clustering")
# Initialize the Seurat object with the raw (non-normalized data).
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)Feature names cannot have underscores ('_'), replacing with dashes ('-')# The [[ operator can add columns to object metadata. This is a great place to stash QC stats
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")# Visualize QC metrics as a violin plot
VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
# Normalizing the data
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|# Identification of highly variable features
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)Calculating gene variances
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**************************************************|
Calculating feature variances of standardized and clipped values
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**************************************************|# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(pbmc), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(pbmc)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.When using repel, set xnudge and ynudge to 0 for optimal resultsplot1 + plot2
# Scaling the data
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)Centering and scaling data matrix
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|==================================================================| 100%# Perform linear dimensional reduction (PCA)
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))PC_ 1
Positive: MALAT1, LTB, IL32, CD2, ACAP1, STK17A, CTSW, CD247, CCL5, GIMAP5
AQP3, GZMA, CST7, TRAF3IP3, MAL, HOPX, ITM2A, GZMK, MYC, BEX2
GIMAP7, ETS1, LDLRAP1, ZAP70, LYAR, RIC3, TNFAIP8, KLRG1, SAMD3, NKG7
Negative: CST3, TYROBP, LST1, AIF1, FTL, FCN1, LYZ, FTH1, S100A9, FCER1G
TYMP, CFD, LGALS1, CTSS, S100A8, SERPINA1, LGALS2, SPI1, IFITM3, PSAP
CFP, SAT1, IFI30, COTL1, S100A11, NPC2, LGALS3, GSTP1, PYCARD, NCF2
PC_ 2
Positive: CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DRA, HLA-DQB1, LINC00926, CD79B, HLA-DRB1, CD74
HLA-DPB1, HLA-DMA, HLA-DQA2, HLA-DRB5, HLA-DPA1, HLA-DMB, FCRLA, HVCN1, LTB, BLNK
KIAA0125, P2RX5, IRF8, IGLL5, SWAP70, ARHGAP24, SMIM14, PPP1R14A, FCRL2, C16orf74
Negative: NKG7, PRF1, CST7, GZMA, GZMB, FGFBP2, CTSW, GNLY, GZMH, SPON2
CCL4, FCGR3A, CCL5, CD247, XCL2, CLIC3, AKR1C3, SRGN, HOPX, CTSC
TTC38, S100A4, ANXA1, IL32, IGFBP7, ID2, ACTB, XCL1, APOBEC3G, SAMD3
PC_ 3
Positive: HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1, CD74, HLA-DPA1, MS4A1, HLA-DRB1, HLA-DRB5
HLA-DRA, HLA-DQA2, TCL1A, LINC00926, HLA-DMB, HLA-DMA, HVCN1, FCRLA, IRF8, BLNK
KIAA0125, SMIM14, PLD4, IGLL5, P2RX5, TMSB10, SWAP70, LAT2, MALAT1, IGJ
Negative: PPBP, PF4, SDPR, SPARC, GNG11, NRGN, GP9, RGS18, TUBB1, CLU
HIST1H2AC, AP001189.4, ITGA2B, CD9, TMEM40, CA2, PTCRA, ACRBP, MMD, TREML1
NGFRAP1, F13A1, RUFY1, SEPT5, MPP1, CMTM5, TSC22D1, MYL9, RP11-367G6.3, GP1BA
PC_ 4
Positive: HLA-DQA1, CD79A, CD79B, HIST1H2AC, HLA-DQB1, PF4, MS4A1, SDPR, CD74, PPBP
HLA-DPB1, GNG11, HLA-DQA2, SPARC, HLA-DRB1, HLA-DPA1, GP9, TCL1A, HLA-DRA, LINC00926
NRGN, RGS18, HLA-DRB5, PTCRA, CD9, AP001189.4, CA2, CLU, TUBB1, ITGA2B
Negative: VIM, S100A8, S100A6, S100A4, S100A9, TMSB10, IL32, GIMAP7, LGALS2, S100A10
RBP7, FCN1, MAL, LYZ, S100A12, MS4A6A, CD2, FYB, S100A11, FOLR3
GIMAP4, AQP3, ANXA1, AIF1, MALAT1, GIMAP5, IL8, IFI6, TRABD2A, TMSB4X
PC_ 5
Positive: GZMB, FGFBP2, NKG7, GNLY, PRF1, CCL4, CST7, SPON2, GZMA, CLIC3
GZMH, XCL2, CTSW, TTC38, AKR1C3, CCL5, IGFBP7, XCL1, CCL3, S100A8
TYROBP, HOPX, CD160, HAVCR2, S100A9, FCER1G, PTGDR, LGALS2, RBP7, S100A12
Negative: LTB, VIM, AQP3, PPA1, MAL, KIAA0101, CD2, CYTIP, CORO1B, FYB
IL32, TRADD, ANXA5, TUBA1B, HN1, TYMS, PTGES3, ITM2A, COTL1, GPR183
TNFAIP8, ACTG1, TRAF3IP3, ATP5C1, GIMAP4, ZWINT, PRDX1, LDLRAP1, ABRACL, NGFRAP1 # Examine and visualize PCA results a few different ways
print(pbmc[["pca"]], dims = 1:5, nfeatures = 5)PC_ 1
Positive: MALAT1, LTB, IL32, CD2, ACAP1
Negative: CST3, TYROBP, LST1, AIF1, FTL
PC_ 2
Positive: CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DRA
Negative: NKG7, PRF1, CST7, GZMA, GZMB
PC_ 3
Positive: HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1
Negative: PPBP, PF4, SDPR, SPARC, GNG11
PC_ 4
Positive: HLA-DQA1, CD79A, CD79B, HIST1H2AC, HLA-DQB1
Negative: VIM, S100A8, S100A6, S100A4, S100A9
PC_ 5
Positive: GZMB, FGFBP2, NKG7, GNLY, PRF1
Negative: LTB, VIM, AQP3, PPA1, MAL VizDimLoadings(pbmc, dims = 1:2, reduction = "pca")
DimPlot(pbmc, reduction = "pca")
DimHeatmap(pbmc, dims = 1, cells = 500, balanced = TRUE)
DimHeatmap(pbmc, dims = 1:15, cells = 500, balanced = TRUE)
# Determine the 'dimensionality of the dataset
# NOTE: This process can take a long time for big datasets, comment out for expediency. More
# approximate techniques such as those implemented in ElbowPlot() can be used to reduce
# computation time
pbmc <- JackStraw(pbmc, num.replicate = 100)
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s pbmc <- ScoreJackStraw(pbmc, dims = 1:20)
JackStrawPlot(pbmc, dims = 1:15)
ElbowPlot(pbmc)
# Cluster the cell
pbmc <- FindNeighbors(pbmc, dims = 1:10)Computing nearest neighbor graph
Computing SNNpbmc <- FindClusters(pbmc, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 2700
Number of edges: 97958
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8717
Number of communities: 9
Elapsed time: 0 seconds# Look at cluster IDs of the first 5 cells
head(Idents(pbmc), 5)AAACATACAACCAC-1 AAACATTGAGCTAC-1 AAACATTGATCAGC-1 AAACCGTGCTTCCG-1
0 3 2 5
AAACCGTGTATGCG-1
6
Levels: 0 1 2 3 4 5 6 7 8# Run non-linear dimensional reduction
# If you haven't installed UMAP, you can do so via reticulate::py_install(packages =
# 'umap-learn')
pbmc <- RunUMAP(pbmc, dims = 1:10)The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session21:05:30 UMAP embedding parameters a = 0.9922 b = 1.112
21:05:30 Read 2700 rows and found 10 numeric columns
21:05:30 Using Annoy for neighbor search, n_neighbors = 30
21:05:30 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:05:30 Writing NN index file to temp file /var/folders/qh/0y9_79cs529bxk6gkx7h0m6m0000gn/T//RtmpkL885w/file1faa6f1ee580
21:05:30 Searching Annoy index using 1 thread, search_k = 3000
21:05:31 Annoy recall = 100%
21:05:31 Commencing smooth kNN distance calibration using 1 thread
21:05:32 Initializing from normalized Laplacian + noise
21:05:32 Commencing optimization for 500 epochs, with 107870 positive edges
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:05:36 Optimization finished# note that you can set `label = TRUE` or use the LabelClusters function to help label
# individual clusters
DimPlot(pbmc, reduction = "umap")
# find all markers of cluster 1
cluster1.markers <- FindMarkers(pbmc, ident.1 = 1, min.pct = 0.25)
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cluster5.markers <- FindMarkers(pbmc, ident.1 = 5, ident.2 = c(0, 3), min.pct = 0.25)
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s head(cluster5.markers, n = 5)# find markers for every cluster compared to all remaining cells, report only the positive ones
pbmc.markers <- FindAllMarkers(pbmc, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
| | 0 % ~calculating
|+ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 8 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 13% ~01s
|++++++++ | 14% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 41% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|+++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 5 % ~02s
|+++ | 6 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 20% ~01s
|+++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~00s
|++ | 2 % ~00s
|++ | 3 % ~00s
|+++ | 4 % ~00s
|+++ | 5 % ~00s
|++++ | 6 % ~00s
|++++ | 7 % ~00s
|+++++ | 8 % ~00s
|+++++ | 9 % ~00s
|++++++ | 10% ~00s
|++++++ | 11% ~00s
|+++++++ | 12% ~00s
|+++++++ | 13% ~00s
|++++++++ | 14% ~00s
|++++++++ | 15% ~00s
|+++++++++ | 16% ~00s
|+++++++++ | 17% ~00s
|++++++++++ | 18% ~00s
|++++++++++ | 19% ~00s
|+++++++++++ | 20% ~00s
|+++++++++++ | 21% ~00s
|++++++++++++ | 22% ~00s
|++++++++++++ | 23% ~00s
|+++++++++++++ | 24% ~00s
|+++++++++++++ | 26% ~00s
|++++++++++++++ | 27% ~00s
|++++++++++++++ | 28% ~00s
|+++++++++++++++ | 29% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++ | 32% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++ | 34% ~00s
|++++++++++++++++++ | 35% ~00s
|++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 37% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 39% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 41% ~00s
|+++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 43% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|+++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|+++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 3
| | 0 % ~calculating
|+ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|+++++ | 8 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 13% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 23% ~00s
|+++++++++++++ | 24% ~00s
|+++++++++++++ | 26% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++ | 32% ~00s
|+++++++++++++++++ | 34% ~00s
|++++++++++++++++++ | 35% ~00s
|+++++++++++++++++++ | 37% ~00s
|++++++++++++++++++++ | 39% ~00s
|+++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 4
| | 0 % ~calculating
|+ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~00s
|++++++++++++++++ | 32% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++ | 35% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 39% ~00s
|+++++++++++++++++++++ | 41% ~00s
|++++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 6 % ~02s
|++++ | 7 % ~03s
|+++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 13% ~02s
|++++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|+++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 5 % ~02s
|+++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 10% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 33% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 13% ~02s
|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|+++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 37% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
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|++++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
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|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
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|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
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|+++++++++++++++++++++++++ | 48% ~01s
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|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
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|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
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|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
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|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
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|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s pbmc.markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_logFC)cluster1.markers <- FindMarkers(pbmc, ident.1 = 0, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)
| | 0 % ~calculating
|+ | 1 % ~00s
|++ | 2 % ~00s
|++ | 3 % ~00s
|+++ | 5 % ~00s
|+++ | 6 % ~00s
|++++ | 7 % ~00s
|++++ | 8 % ~00s
|+++++ | 9 % ~00s
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|++++++ | 11% ~00s
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|++++++++ | 15% ~00s
|++++++++ | 16% ~00s
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|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|+++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
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|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
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|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
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|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s VlnPlot(pbmc, features = c("MS4A1", "CD79A"))
# you can plot raw counts as well
VlnPlot(pbmc, features = c("NKG7", "PF4"), slot = "counts", log = TRUE)
FeaturePlot(pbmc, features = c("MS4A1", "GNLY", "CD3E", "CD14", "FCER1A", "FCGR3A", "LYZ", "PPBP",
"CD8A"))
top10 <- pbmc.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
DoHeatmap(pbmc, features = top10$gene) + NoLegend()
new.cluster.ids <- c("Naive CD4 T", "Memory CD4 T", "CD14+ Mono", "B", "CD8 T", "FCGR3A+ Mono",
"NK", "DC", "Platelet")
names(new.cluster.ids) <- levels(pbmc)
pbmc <- RenameIdents(pbmc, new.cluster.ids)
DimPlot(pbmc, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
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